Practice Lesson 2:
Inductive Analytics

Packages

## load required libraries
library(tidyverse)
library(quanteda)
library(lexicon)
library(reshape2)
library(stringi)
library(quanteda.textplots)
library(quanteda.textmodels)
library(quanteda.textstats)
library(gridExtra)
library(seededlda)
library(ggrepel)
library(ggdendro)
library(factoextra)
library(lattice)
library(spacyr)

Clean workspace and set working directory

## clean workspace
rm(list=ls())

## set working directory (WD)
path <- '~/coliphi21/practice_lessons/lesson_2/src/'
## you can also set it dynamically: 
## setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
setwd(path)

## check that WD is set correctly
getwd()

## [1] "/Users/lucienbaumgartner/coliphi21/practice_lessons/lesson_2/src"

Import data

For this tutorial you can either work with your own data, or the pre-built copora provided in the /input-folder for the first practice session. The quanteda-package also contains pre-built corpora you can use. For this session, we scraped the Stanford Encyclopedia of Philosophy and built a corpus including additional metadata.

## relative path
load('../input/stanford-encyclopedia.RDS')

## absolute path
load('~/coliphi21/practice_lessons/lesson_2/input/stanford-encyclopedia.RDS')

Inspect data

## how does the corpus object look like?
sfe

## Corpus consisting of 1,712 documents and 21 docvars.
## 18thGerman-preKant.json :
## " In Germany, the eighteenth century was the age of enlighten..."
## 
## abduction.json :
## " In the philosophical literature, the term abduction is used..."
## 
## abelard.json :
## " Peter Abelard (1079–21 April 1142) [Abailard or Abaelard or..."
## 
## abhidharma.json :
## " The first centuries after Śākyamuni Buddha death saw the ri..."
## 
## abilities.json :
## " In the accounts we give of one another, claims about our ab..."
## 
## abner-burgos.json :
## " Abner of Burgos (Alfonso de Valladolid; c. 1260–1347) was p..."
## 
## [ reached max_ndoc ... 1,706 more documents ]

## summary statistics
summary(sfe) %>% head

## available variables
docvars(sfe)

Prep

## tokenization
toks <- tokens(sfe, what = 'word',
               remove_punct = T, remove_symbols = T, padding = F, 
               remove_numbers = T, remove_url = T)
## to lower
toks <- tokens_tolower(toks)
## lemmatizing
toks <- tokens_replace(toks, 
                       pattern = lexicon::hash_lemmas$token, 
                       replacement = lexicon::hash_lemmas$lemma)
## remove stopwords
toks <- tokens_select(toks,  pattern = stopwords("en"), selection = "remove")
## remove noise
toks <- tokens_select(toks, pattern = '^[A-z]$|[0-9]+|^.$', valuetype = 'regex', selection = 'remove')
## create dfm
dfm_sfe <- dfm(toks) %>% 
           dfm_trim(min_termfreq = 0.8, termfreq_type = "quantile",
                    max_docfreq = 0.1, docfreq_type = "prop")

dfm_sfe

## Document-feature matrix of: 1,712 documents, 24,689 features (98.47% sparse) and 21 docvars.
##                          features
## docs                      ethos immanuel thomasius pietist thomasians wolff well dis halle pietism
##   18thGerman-preKant.json     2        1        33       6         11    36    0   1    19       7
##   abduction.json              0        0         0       0          0     0    0   0     0       0
##   abelard.json                0        0         0       0          0     0    0   0     0       0
##   abhidharma.json             0        0         0       0          0     0    0   0     0       0
##   abilities.json              0        0         0       0          0     0    0   0     0       0
##   abner-burgos.json           0        0         0       0          0     0    0   0     0       0
## [ reached max_ndoc ... 1,706 more documents, reached max_nfeat ... 24,679 more features ]

## remove phi
toks <- tokens_select(toks, pattern = 'φ', valuetype = 'regex', selection = 'remove')
## create dfm
dfm_sfe <- dfm(toks) %>% 
           dfm_trim(min_termfreq = 0.8, termfreq_type = "quantile",
                    max_docfreq = 0.1, docfreq_type = "prop")

Scaling: correspondence analysis

## compute model
sfe_ca <- textmodel_ca(dfm_sfe)

## coerce model coefficients to dataframe
sfe_ca <- data.frame(dim1 = coef(sfe_ca, doc_dim = 1)$coef_document, 
                     dim2 = coef(sfe_ca, doc_dim = 2)$coef_document)

sfe_ca$id <- gsub('\\.json(\\.[0-9])?', '', rownames(sfe_ca))
sfe_ca

## plot full data with branch annotation
ggplot(sfe_ca, aes(x=dim1, y=dim2, label=id)) +
  geom_point(aes(color=dim1-dim2), alpha = 0.2) +
  # plot 0.2 of all labels, using a repel function
  geom_text_repel(data = dplyr::sample_frac(sfe_ca, 0.2), max.overlaps = 15, seed = 6734) +
  theme_bw() +
  theme(plot.title = element_text(face='bold')) +
  labs(title = 'Correspondence Analysis: Full Data')

## plot parts of the data
ggplot(sfe_ca, aes(x=dim1, y=dim2, label=id)) +
  geom_point(aes(color=dim1-dim2), alpha = 0.2) +
  # plot 0.2 of all labels, using a repel function
  geom_text_repel(data = dplyr::sample_frac(sfe_ca, 0.2), max.overlaps = 9, seed = 6734) +
  scale_y_continuous(limits=c(-2,0)) +
  scale_x_continuous(limits=c(-1,1)) +
  theme_bw() +
  theme(plot.title = element_text(face='bold')) +
  labs(title = 'Correspondence Analysis: Zoom')

Unsupervised LDA

## run naive unsupervised topic model with 10 topics
set.seed(123)
sfe_lda <- textmodel_lda(dfm_sfe, k = 10)

## print top 20 terms per topic
terms(sfe_lda, 20)

##       topic1          topic2         topic3           topic4           topic5        topic6        topic7             topic8         topic9        topic10     
##  [1,] "supervenience" "gene"         "disability"     "turing"         "spacetime"   "ockham"      "chinese"          "heidegger"    "privacy"     "ibn"       
##  [2,] "trope"         "molecular"    "oppression"     "gödel"          "einstein"    "bacon"       "spinoza"          "dewey"        "theism"      "avicenna"  
##  [3,] "monism"        "neural"       "african"        "algebra"        "kuhn"        "scotus"      "reid"             "husserl"      "torture"     "buddhist"  
##  [4,] "fictional"     "simulation"   "racial"         "intuitionistic" "popper"      "pythagoras"  "dao"              "du"           "user"        "maimonides"
##  [5,] "bolzano"       "dna"          "coercion"       "computation"    "reichenbach" "boethius"    "nietzsche"        "malebranche"  "clinical"    "arabic"    
##  [6,] "brentano"      "darwin"       "feminism"       "ordinal"        "weyl"        "pythagorean" "confucian"        "artist"       "whitehead"   "averroes"  
##  [7,] "goodman"       "fitness"      "capability"     "hilbert"        "gravity"     "parmenides"  "sidgwick"         "berlin"       "delusion"    "japanese"  
##  [8,] "physicalism"   "inheritance"  "domination"     "cardinal"       "hole"        "proclus"     "utilitarianism"   "artistic"     "theist"      "buddha"    
##  [9,] "strawson"      "fodor"        "pornography"    "tarski"         "ramsey"      "cicero"      "mohists"          "spinoza"      "theistic"    "buddhism"  
## [10,] "noun"          "ai"           "distributive"   "peirce"         "bayesian"    "philo"       "conscience"       "wolff"        "hartshorne"  "dharma"    
## [11,] "bradley"       "artifact"     "marx"           "algorithm"      "entropy"     "plotinus"    "consequentialist" "nietzsche"    "enhancement" "islamic"   
## [12,] "austin"        "imagery"      "egalitarian"    "algebraic"      "bohr"        "sextus"      "luck"             "herder"       "doxastic"    "indian"    
## [13,] "armstrong"     "biologist"    "dworkin"        "recursive"      "newtonian"   "porphyry"    "laozi"            "clarke"       "omnipotent"  "emptiness" 
## [14,] "chisholm"      "drift"        "coercive"       "brouwer"        "payoff"      "abelard"     "zhuangzi"         "sartre"       "embryo"      "mystical"  
## [15,] "entailment"    "biodiversity" "sovereign"      "provable"       "feyerabend"  "bce"         "zhu"              "romantic"     "suicide"     "nishida"   
## [16,] "plural"        "cancer"       "income"         "zfc"            "dynamical"   "iamblichus"  "thick"            "bois"         "internalism" "japan"     
## [17,] "intension"     "quale"        "constitutional" "computable"     "bell"        "fr"          "hutcheson"        "artwork"      "gratitude"   "zen"       
## [18,] "implicature"   "adaptation"   "liberalism"     "cantor"         "gas"         "luther"      "confucius"        "fichte"       "engine"      "hebrew"    
## [19,] "grice"         "genome"       "anderson"       "diagram"        "mach"        "timaeus"     "relativism"       "schopenhauer" "csm"         "vasubandhu"
## [20,] "meinong"       "digital"      "republican"     "definable"      "bet"         "sophist"     "wang"             "collins"      "goldman"     "al-fārābī"

## plot the topics over the correspondence analysis data
sfe_ca$topics <- topics(sfe_lda)
ggplot(sfe_ca, aes(x=dim1, y=dim2, color=topics)) +
  geom_point(alpha = 0.5, shape = '.') +
  geom_density_2d(alpha = 0.5) +
  theme_bw() +
  theme(plot.title = element_text(face='bold')) +
  labs(title = 'Correspondence Analysis with Topic Annotation (k=10)')

sfe_ca$topics <- recode(sfe_ca$topics, topic1 = "body-mind", topic2 = "biology", 
                        topic3 = "feminism/critical thinking", topic4 = "math/ai", 
                        topic5 = "physics", topic6 = "classics", topic7 = "eastern",
                        topic8 = "phenomenology", topic9 = "religion", 
                        topic10 = "middle-eastern/eastern")

PoS-tagging - leaving the sandbox

## set seed
set.seed(48621)
## draw a random sample of 20 documents
sfe_sub <- sfe[sample(1:length(sfe), 5)]
sfe_sub

## Corpus consisting of 5 documents and 21 docvars.
## albert-saxony.json :
## " Albert of Saxony (ca. 1320–1390), Master of Arts at Paris, ..."
## 
## contractarianism.json.1 :
## " Contractarianism names both a political theory of the legit..."
## 
## preferences.json.1 :
## " The notion of preference has a central role in many discipl..."
## 
## plotinus.json :
## " Plotinus (204/5 – 270 C.E.), is generally regarded as the f..."
## 
## paternalism.json :
## " Paternalism is the interference of a state or an individual..."

## PoS-tagging
sfe_pos <- spacy_parse(sfe_sub, pos = T, tag = T, lemma = T, entity = T, dependency = T)
sfe_pos

## look up which adjectives are used most frequently
sfe_pos %>% 
  filter(pos == 'ADJ') %>% 
  group_by(token) %>% 
  summarise(n.occurences = n()) %>% 
  arrange(desc(n.occurences))

## look up which nouns are preceded by the adjective "rational"
rational_noun <- sfe_pos %>% filter(pos == 'NOUN' & lag(token, 1) == 'rational')
rational_noun

# get top 2 nouns per document
rational_noun %>% 
  group_by(doc_id, token) %>% 
  summarise(n.occurences = n()) %>% 
  arrange(doc_id, desc(n.occurences)) %>% 
  slice(1:2)

Augment your sandbox

## to create a corpus-object from your pos-tagged tokens
## we need unique IDs
sfe_pos

## make doc_ids unique
sfe_pos <- mutate(sfe_pos, doc_id = make.unique(doc_id))
## remove punctuation and spaces
sfe_pos <- filter(sfe_pos, !pos %in% c('PUNCT', 'SPACE'))
## make token corpus
sfe_pos <- corpus(sfe_pos, text_field = 'token', docid_field = 'doc_id')
sfe_pos

## Corpus consisting of 35,214 documents and 8 docvars.
## albert-saxony.json.1 :
## "Albert"
## 
## albert-saxony.json.2 :
## "of"
## 
## albert-saxony.json.3 :
## "Saxony"
## 
## albert-saxony.json.5 :
## "ca"
## 
## albert-saxony.json.6 :
## "."
## 
## albert-saxony.json.7 :
## "1320–1390"
## 
## [ reached max_ndoc ... 35,208 more documents ]

docvars(sfe_pos)

## WARNING! This data-structure is incompatible with our document-based corpus!!!
docvars(sfe_sub)

## ... but we can add the info to our token corpus
# add initial document ID to both sets of docvars
docvars(sfe_pos)$initial_docid <- gsub('\\.json.*', '', docid(sfe_pos))
docvars(sfe_sub)$initial_docid <- gsub('\\.json.*', '', docid(sfe_sub))
# join by initial id
docvars(sfe_pos) <- left_join(docvars(sfe_pos), docvars(sfe_sub), by = 'initial_docid')
docvars(sfe_pos)

## Keep in mind: your corpus is still on token level!

Additional material

## hierarchical clustering - get distances on normalized dfm
sfe_dist_mat <- dfm_weight(dfm_sfe, scheme = "prop") %>%
    textstat_dist(method = "euclidean") %>% 
    as.dist()
## hiarchical clustering the distance object
sfe_cluster <- hclust(sfe_dist_mat, method = 'ward.D')
# label with document names
sfe_cluster$labels <- gsub('\\.json(\\.[0-9])?', '', docnames(dfm_sfe))
## determine best numbers of clusters
# fviz_nbclust(as.matrix(sfe_dist_mat), FUN = hcut, method = "wss")
## cut tree into four groups
clusters <- cutree(sfe_cluster, k = 4)
## add cluster-data to the correspondence analysis
sfe_ca_hcl <- left_join(sfe_ca, data.frame(cluster = clusters, id = names(clusters)))
## plot
ggplot(sfe_ca_hcl, aes(x=dim1, y=dim2, label=id)) +
  geom_point(aes(color=as.factor(cluster)), alpha = 0.2) +
  facet_grid(~as.factor(cluster))

## hierarchical clustering doesn't provide discrete cluster along
## the dimensions of the correspondance analysis

## subset documents about logic
logic <- dfm_subset(dfm_sfe, grepl('(?<=\\-)logic|logic(?=\\-)', docnames(dfm_sfe), perl = T))
## compute cosine similarity
logic_sim <- textstat_simil(logic, margin = 'document', method = 'cosine')
## all pairs with a cosine similarity > .4
as.data.frame(logic_sim) %>% 
  filter(cosine > .4) %>% 
  arrange(desc(cosine))

## subset documents about aesthetics
aesth <- dfm_subset(dfm_sfe, grepl('aesthetics', docnames(dfm_sfe), perl = T))
## compute cosine similarity
aesth <- textstat_simil(aesth, margin = 'document', method = 'cosine')
## all pairs with a cosine similarity > .2
as.data.frame(aesth) %>% 
  filter(cosine > .2) %>% 
  arrange(desc(cosine))

## subset documents about feminism
fem <- dfm_subset(dfm_sfe, grepl('(?<=\\-)fem|fem.*(?=\\-)', docnames(dfm_sfe), perl = T))
## compute cosine similarities for the features 
## "empowerment", "embodiment", and "rape"
fem_sim <- textstat_simil(logic, logic[, c("empowerment", "embodiment", "rape")], 
                          margin = 'feature', method = 'cosine')
## top 5 results per feature
as.data.frame(fem_sim) %>% 
  group_by(feature2) %>% 
  arrange(feature2, desc(cosine)) %>% 
  slice_head(n=5)

fem_sim <- textstat_simil(logic, logic[, c("feminism", "patriarchy")], 
                          margin = 'feature', method = 'cosine')
## top 5 results per feature
as.data.frame(fem_sim) %>% 
  group_by(feature2) %>% 
  arrange(feature2, desc(cosine)) %>% 
  slice_head(n=5)